library(nycflights13)
library(tidyverse)
Registered S3 methods overwritten by 'ggplot2':
  method         from 
  [.quosures     rlang
  c.quosures     rlang
  print.quosures rlang
── Attaching packages ─────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
✔ tidyr   0.8.3     ✔ stringr 1.3.1
✔ readr   1.1.1     ✔ forcats 0.3.0
── Conflicts ────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
install.packages("nycflights13")
Error in install.packages : Updating loaded packages
"nycflights13"
[1] "nycflights13"

5.2.4 Exercises

1

?flights
flights
filter(flights, arr_delay >= 2) #1
filter(flights, dest == "IAH") #2
airlines
filter(flights, (carrier == "UA" | carrier == "AA" | carrier == "DL")) #3
filter(flights, (month == 7 | month == 8 | month == 9)) #4
filter(flights, arr_delay > 2, dep_delay == 0) #5
filter(flights, dep_delay <= 1, air_time >= 30) #6
filter(flights, (dep_time >= 0000 | dep_time <= 600)) #7

2


Restarting R session...

It finds the values between two values. This code could have been useful for number 7.

3

dep<-select(flights, dep.time)
is.na.data.frame(flights)
           year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay
     [1,] FALSE FALSE FALSE    FALSE          FALSE     FALSE    FALSE          FALSE     FALSE
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          carrier flight tailnum origin  dest air_time distance  hour minute time_hour
     [1,]   FALSE  FALSE   FALSE  FALSE FALSE    FALSE    FALSE FALSE  FALSE     FALSE
     [2,]   FALSE  FALSE   FALSE  FALSE FALSE    FALSE    FALSE FALSE  FALSE     FALSE
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 [ reached getOption("max.print") -- omitted 336724 rows ]

4

These aren’t missing because those would have to be exactly present in the data.

5.3.1 Exercises

1

arrange(flights)
arrange(flights, desc(dep_delay)) #2
arrange(flights, desc(air_time)) #3

4

Flight 15 travelled the longest while flight 167 traveled the shortest.

5.4.1 Exercises

2

select(flights, dep.time, dep_delay, arr_time, arr_delay)

This code only shows the values from the columns that I put in the code.

3

4

This doesn’t surprise me because every column shown has “time” in the title just like we had asked for in the code.

5.5.2 Exercises

1.

transmute(flights,
       sched_arr_time - dep_time)

2.

select(flights, air_time)
transmute(flights, 
            arr_time - dep_time)

The calculation has larger numbers.

3

select(flights, dep_time, sched_dep_time, dep_delay)

dep_time should be the same number as sched_dep_time and dep_delay should be zero.

4

?min_rank

5

1:3 + 1:10
longer object length is not a multiple of shorter object length
 [1]  2  4  6  5  7  9  8 10 12 11

Results in an error because these ratios cannot be used as values to use for calculations.

6

R provides tangent, cosine, sin, and their inverses.

5.6.7 Exercises

1

You can use group_by(), summarise(), count(), sum(), and ungroup() Departure delay is more important.

2

3

Departure delay is more important.

6

?sort

It sorts data frames.

5.7.1 Exercises

1

When mutate is combined to grouping, the new column made is grouped and ordered by the variables given in the code.

2

arrange(flights, desc(tailnum))

3

arrange(flights, min_rank(dep_delay))

You should leave around 8pm.

4

flights %>%
  group_by(dest) %>%
  select(dest)

5

?lag
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